Multiple transform sizes improve video coding efficiency, but also increase the implementation complexity. Furthermore, both forward and inverse transforms need to be supported in various consumer devices. Embodiments provide a unified forward and inverse transform architecture that supports computation of both forward and inverse transforms for multiple transforms sizes using shared hardware circuits. The unified architecture exploits the symmetry properties of forward and inverse transform matrices to achieve hardware sharing across different the transform sizes and also between forward and inverse transform computations.
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2. The method of claim 1, wherein determining reconstructed residual values for the coding unit based on the matrix-multiplied odd vector and the matrix-multiplied even vector comprises selectively adding and subtracting elements of the matrix-multiplied odd vector and the matrix-multiplied even vector.
This invention relates to video coding, specifically improving the reconstruction of residual data in block-based video compression. The problem addressed is efficiently reconstructing residual values for a coding unit after transform and quantization, which is a critical step in reducing computational complexity while maintaining reconstruction accuracy. The method involves processing residual data by separating it into odd and even components, applying matrix multiplication to each, and then combining these components to reconstruct the original residual values. The reconstruction step selectively adds and subtracts elements from the matrix-multiplied odd and even vectors to produce the final residual values. This selective combination ensures accurate reconstruction while optimizing computational efficiency. The matrix multiplication operations are applied to the odd and even vectors separately, transforming them into a form that allows for efficient combination. The selective addition and subtraction of elements from these transformed vectors ensures that the reconstructed residual values accurately represent the original residual data, which is essential for high-quality video reconstruction. This approach reduces the computational overhead associated with traditional reconstruction methods by leveraging matrix operations and selective combination, making it particularly useful in real-time video encoding and decoding applications. The method is designed to work within existing video coding frameworks, such as those used in standards like H.264 or HEVC, but with improved efficiency.
5. The system of claim 4, wherein determining an output vector based on the matrix-multiplied odd vector and the matrix-multiplied even vector comprises selectively adding and subtracting elements of the matrix-multiplied odd vector and the matrix-multiplied even vector.
This invention relates to a signal processing system designed to enhance the efficiency of vector computations, particularly in applications involving matrix multiplications and vector operations. The system addresses the computational overhead and complexity associated with traditional matrix-vector multiplication, which often requires extensive processing resources and time. The system processes input vectors by separating them into odd and even components. Each component is then independently matrix-multiplied with a predefined transformation matrix. The resulting matrix-multiplied odd and even vectors are subsequently combined to produce an output vector. The combination process involves selectively adding and subtracting corresponding elements of the matrix-multiplied odd and even vectors, allowing for optimized computation and reduced processing load. By decomposing the input vector into odd and even parts before matrix multiplication, the system minimizes redundant calculations and leverages parallel processing capabilities. This approach improves computational efficiency, especially in high-performance computing environments where real-time processing is critical. The selective addition and subtraction of vector elements further refines the output, ensuring accuracy while reducing the number of operations required. The system is particularly useful in applications such as digital signal processing, machine learning, and real-time data analysis, where fast and efficient vector computations are essential.
14. The system of claim 13, wherein determining reconstructed residual values for the coding unit based on the matrix-multiplied odd vector and the matrix-multiplied even vector comprises selectively adding and subtracting elements of the matrix-multiplied odd vector and the matrix-multiplied even vector.
This invention relates to video coding systems, specifically methods for reconstructing residual values in a coding unit during video compression. The problem addressed is improving computational efficiency and accuracy in reconstructing residual data, which is critical for maintaining video quality while reducing processing overhead. The system processes a coding unit by separating it into odd and even indexed elements, which are then transformed using matrix multiplication. The key innovation involves reconstructing the residual values by selectively adding and subtracting elements from the transformed odd and even vectors. This selective operation ensures precise reconstruction while minimizing computational complexity. The method leverages matrix operations to efficiently combine the transformed vectors, avoiding redundant calculations and improving decoding speed. The system also includes a step of determining a prediction mode for the coding unit, which influences how the residual values are processed. The prediction mode helps adapt the reconstruction process to different types of video content, further optimizing performance. The overall approach enhances video compression efficiency by reducing the computational burden while maintaining high-quality reconstruction of residual data. This is particularly useful in real-time video applications where processing speed and accuracy are critical.
19. The system of claim 13, further comprising a liquid crystal display, coupled to the decoder, for displaying the displayable image from the reconstructed coding unit.
A system for image processing and display includes a decoder that reconstructs a coding unit from encoded image data. The system further comprises a liquid crystal display (LCD) coupled to the decoder, which displays the reconstructed image. The decoder processes the encoded data to generate a displayable image, which is then rendered on the LCD. This system is designed to handle encoded image data, decode it, and present the resulting image on a display device. The LCD is directly connected to the decoder to ensure real-time or near-real-time display of the reconstructed image. The system may be part of a larger imaging or multimedia device, such as a digital camera, video player, or display monitor, where efficient decoding and display of images are essential. The inclusion of the LCD allows for immediate visualization of the decoded content, making it suitable for applications requiring fast and accurate image rendering. The system ensures that the reconstructed image is accurately displayed without significant latency, enhancing user experience in devices that rely on real-time image processing.
20. The method of claim 1, further comprising displaying the displayable image from the reconstructed coding unit by a liquid crystal display.
A method for image processing and display involves reconstructing a coding unit from encoded image data and displaying the resulting image on a liquid crystal display (LCD). The method addresses the challenge of efficiently decoding and presenting digital image data, particularly in systems where display quality and processing speed are critical. The reconstruction process involves decoding the encoded image data to generate a displayable image, which is then rendered on the LCD. This approach ensures accurate and timely presentation of visual information, improving user experience in applications such as digital signage, medical imaging, or consumer electronics. The method may also include additional steps such as error correction, color calibration, or dynamic contrast adjustment to enhance display performance. By integrating the LCD as the final output device, the method provides a complete solution for converting encoded image data into a visually perceptible format, optimizing both processing efficiency and display quality.
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August 30, 2021
May 28, 2024
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